predicted contacts and 3D models of 510 non-redundant membrane proteins with solved structures in PDBTM

1) The contacts are predicted by the deep learning method developed by Dr. Jinbo Xu. Each protein has a .gcnn file, which is a text file containing a L*L matrix. Meanwhile, L is the protein sequence length, each entry in the matrix is a predicted probability of the corresponding residue pair forming a contact. Please ignore those entries (i,j) where abs(i-j)<6 . 2) The 3D models generated by CNS software from predicted contacts and secondary structure. Each predicted 3D model is a PDB file. For each protein, 5 models are predicted.1) 该蛋白质接触图谱由徐金波博士开发的深度学习方法预测得到。每个蛋白质对应一个.gcnn文件，该文件为文本格式，内含一个L×L的矩阵。其中L为该蛋白质的序列长度，矩阵中的每一个元素代表对应残基对形成接触的预测概率。请忽略满足|i-j|<6的(i,j)矩阵条目。 2) 上述三维结构模型由CNS软件基于预测得到的接触图谱与蛋白质二级结构生成。每个预测得到的三维结构模型均为PDB（Protein Data Bank）格式文件，每个蛋白质可生成5个预测模型。